'''simple docstring'''




import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import DetaImageProcessor




class        a__ (  unittest.TestCase					):






     def __init__(					self						:						Optional[int]    ,							a						:						Optional[Any]    ,							a						:						List[Any]=7    ,							a						:						Dict=3    ,							a						:						List[Any]=30    ,							a						:						Any=4_00    ,							a						:						str=True    ,							a						:						int=None    ,							a						:						List[Any]=True    ,							a						:						Tuple=[0.5, 0.5, 0.5]    ,							a						:						Dict=[0.5, 0.5, 0.5]    ,							a						:						Tuple=True    ,							a						:						Dict=1 / 2_55    ,							a						:						Tuple=True    ,							):





         """simple docstring"""



         __lowerCamelCase            =    size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
         __lowerCamelCase            =    parent
         __lowerCamelCase            =    batch_size
         __lowerCamelCase            =    num_channels
         __lowerCamelCase            =    min_resolution
         __lowerCamelCase            =    max_resolution
         __lowerCamelCase            =    do_resize
         __lowerCamelCase            =    size
         __lowerCamelCase            =    do_normalize
         __lowerCamelCase            =    image_mean
         __lowerCamelCase            =    image_std
         __lowerCamelCase            =    do_rescale
         __lowerCamelCase            =    rescale_factor
         __lowerCamelCase            =    do_pad






     def   SCREAMING_SNAKE_CASE__  (					self						:						Any       ):





         """simple docstring"""



         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_pad": self.do_pad,
         }






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]    ,							a						:						Optional[Any]    ,							a						:						List[Any]=False       ):





         """simple docstring"""



         if not batched:
             __lowerCamelCase            =    image_inputs[0]
             if isinstance(a    ,							Image.Image       ):
                 __lowerCamelCase      ,    __lowerCamelCase            =    image.size
             else:
                 __lowerCamelCase      ,    __lowerCamelCase            =    image.shape[1], image.shape[2]
             if w < h:
                 __lowerCamelCase            =    int(self.size['''shortest_edge'''] * h / w       )
                 __lowerCamelCase            =    self.size['''shortest_edge''']
             elif w > h:
                 __lowerCamelCase            =    self.size['''shortest_edge''']
                 __lowerCamelCase            =    int(self.size['''shortest_edge'''] * w / h       )
             else:
                 __lowerCamelCase            =    self.size['''shortest_edge''']
                 __lowerCamelCase            =    self.size['''shortest_edge''']

         else:
             __lowerCamelCase            =    []
             for image in image_inputs:
                 __lowerCamelCase      ,    __lowerCamelCase            =    self.get_expected_values([image]       )
                 expected_values.append((expected_height, expected_width)       )
             __lowerCamelCase            =    max(a    ,							key=lambda a       : item[0]       )[0]
             __lowerCamelCase            =    max(a    ,							key=lambda a       : item[1]       )[1]

         return expected_height, expected_width




@require_torch
@require_vision
class        a__ (  UpperCAmelCase__				,  unittest.TestCase					):
     lowerCamelCase    :	int				=DetaImageProcessor if is_vision_available() else None






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         __lowerCamelCase            =    DetaImageProcessingTester(self       )






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						str       ):





         """simple docstring"""



         return self.image_processor_tester.prepare_image_processor_dict()






     def   SCREAMING_SNAKE_CASE__  (					self						:						Any       ):





         """simple docstring"""



         __lowerCamelCase            =    self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(a    ,							'''image_mean'''       )       )
         self.assertTrue(hasattr(a    ,							'''image_std'''       )       )
         self.assertTrue(hasattr(a    ,							'''do_normalize'''       )       )
         self.assertTrue(hasattr(a    ,							'''do_resize'''       )       )
         self.assertTrue(hasattr(a    ,							'''do_rescale'''       )       )
         self.assertTrue(hasattr(a    ,							'''do_pad'''       )       )
         self.assertTrue(hasattr(a    ,							'''size'''       )       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						str       ):





         """simple docstring"""



         __lowerCamelCase            =    self.image_processing_class.from_dict(self.image_processor_dict       )
         self.assertEqual(image_processor.size    ,							{'''shortest_edge''': 18, '''longest_edge''': 13_33}       )
         self.assertEqual(image_processor.do_pad    ,							a       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[str]       ):





         """simple docstring"""



         pass






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         __lowerCamelCase            =    self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         __lowerCamelCase            =    prepare_image_inputs(self.image_processor_tester    ,							equal_resolution=a       )
         for image in image_inputs:
             self.assertIsInstance(a    ,							Image.Image       )

         # Test not batched input
         __lowerCamelCase            =    image_processing(image_inputs[0]    ,							return_tensors='''pt'''       ).pixel_values

         __lowerCamelCase      ,    __lowerCamelCase            =    self.image_processor_tester.get_expected_values(a       )

         self.assertEqual(
             encoded_images.shape    ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,							)

         # Test batched
         __lowerCamelCase      ,    __lowerCamelCase            =    self.image_processor_tester.get_expected_values(a    ,							batched=a       )

         __lowerCamelCase            =    image_processing(a    ,							return_tensors='''pt'''       ).pixel_values
         self.assertEqual(
             encoded_images.shape    ,							(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )    ,							)






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         __lowerCamelCase            =    prepare_image_inputs(self.image_processor_tester    ,							equal_resolution=a    ,							numpify=a       )
         for image in image_inputs:
             self.assertIsInstance(a    ,							np.ndarray       )

         # Test not batched input
         __lowerCamelCase            =    image_processing(image_inputs[0]    ,							return_tensors='''pt'''       ).pixel_values

         __lowerCamelCase      ,    __lowerCamelCase            =    self.image_processor_tester.get_expected_values(a       )

         self.assertEqual(
             encoded_images.shape    ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,							)

         # Test batched
         __lowerCamelCase            =    image_processing(a    ,							return_tensors='''pt'''       ).pixel_values

         __lowerCamelCase      ,    __lowerCamelCase            =    self.image_processor_tester.get_expected_values(a    ,							batched=a       )

         self.assertEqual(
             encoded_images.shape    ,							(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )    ,							)






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         __lowerCamelCase            =    self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         __lowerCamelCase            =    prepare_image_inputs(self.image_processor_tester    ,							equal_resolution=a    ,							torchify=a       )
         for image in image_inputs:
             self.assertIsInstance(a    ,							torch.Tensor       )

         # Test not batched input
         __lowerCamelCase            =    image_processing(image_inputs[0]    ,							return_tensors='''pt'''       ).pixel_values

         __lowerCamelCase      ,    __lowerCamelCase            =    self.image_processor_tester.get_expected_values(a       )

         self.assertEqual(
             encoded_images.shape    ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,							)

         # Test batched
         __lowerCamelCase            =    image_processing(a    ,							return_tensors='''pt'''       ).pixel_values

         __lowerCamelCase      ,    __lowerCamelCase            =    self.image_processor_tester.get_expected_values(a    ,							batched=a       )

         self.assertEqual(
             encoded_images.shape    ,							(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )    ,							)






     @slow
     def   SCREAMING_SNAKE_CASE__  (					self						:						Tuple       ):





         """simple docstring"""



         __lowerCamelCase            =    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
         with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''    ,							'''r'''       ) as f:
             __lowerCamelCase            =    json.loads(f.read()       )

         __lowerCamelCase            =    {'''image_id''': 3_97_69, '''annotations''': target}

         # encode them
         __lowerCamelCase            =    DetaImageProcessor()
         __lowerCamelCase            =    image_processing(images=a    ,							annotations=a    ,							return_tensors='''pt'''       )

         # verify pixel values
         __lowerCamelCase            =    torch.Size([1, 3, 8_00, 10_66]       )
         self.assertEqual(encoding['''pixel_values'''].shape    ,							a       )

         __lowerCamelCase            =    torch.tensor([0.27_96, 0.31_38, 0.34_81]       )
         self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,							a    ,							atol=1e-4       )       )

         # verify area
         __lowerCamelCase            =    torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,							a       )       )
         # verify boxes
         __lowerCamelCase            =    torch.Size([6, 4]       )
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,							a       )
         __lowerCamelCase            =    torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,							a    ,							atol=1e-3       )       )
         # verify image_id
         __lowerCamelCase            =    torch.tensor([3_97_69]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,							a       )       )
         # verify is_crowd
         __lowerCamelCase            =    torch.tensor([0, 0, 0, 0, 0, 0]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,							a       )       )
         # verify class_labels
         __lowerCamelCase            =    torch.tensor([75, 75, 63, 65, 17, 17]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,							a       )       )
         # verify orig_size
         __lowerCamelCase            =    torch.tensor([4_80, 6_40]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,							a       )       )
         # verify size
         __lowerCamelCase            =    torch.tensor([8_00, 10_66]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,							a       )       )






     @slow
     def   SCREAMING_SNAKE_CASE__  (					self						:						List[str]       ):





         """simple docstring"""



         __lowerCamelCase            =    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
         with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''    ,							'''r'''       ) as f:
             __lowerCamelCase            =    json.loads(f.read()       )

         __lowerCamelCase            =    {'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}

         __lowerCamelCase            =    pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''       )

         # encode them
         __lowerCamelCase            =    DetaImageProcessor(format='''coco_panoptic'''       )
         __lowerCamelCase            =    image_processing(images=a    ,							annotations=a    ,							masks_path=a    ,							return_tensors='''pt'''       )

         # verify pixel values
         __lowerCamelCase            =    torch.Size([1, 3, 8_00, 10_66]       )
         self.assertEqual(encoding['''pixel_values'''].shape    ,							a       )

         __lowerCamelCase            =    torch.tensor([0.27_96, 0.31_38, 0.34_81]       )
         self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,							a    ,							atol=1e-4       )       )

         # verify area
         __lowerCamelCase            =    torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,							a       )       )
         # verify boxes
         __lowerCamelCase            =    torch.Size([6, 4]       )
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,							a       )
         __lowerCamelCase            =    torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,							a    ,							atol=1e-3       )       )
         # verify image_id
         __lowerCamelCase            =    torch.tensor([3_97_69]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,							a       )       )
         # verify is_crowd
         __lowerCamelCase            =    torch.tensor([0, 0, 0, 0, 0, 0]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,							a       )       )
         # verify class_labels
         __lowerCamelCase            =    torch.tensor([17, 17, 63, 75, 75, 93]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,							a       )       )
         # verify masks
         __lowerCamelCase            =    82_28_73
         self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()    ,							a       )
         # verify orig_size
         __lowerCamelCase            =    torch.tensor([4_80, 6_40]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,							a       )       )
         # verify size
         __lowerCamelCase            =    torch.tensor([8_00, 10_66]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,							a       )       )

'''simple docstring'''




from manim import *




class        a__ (  UpperCAmelCase__					):






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    Rectangle(height=0.5    ,							width=0.5       )
         __lowerCamelCase            =    Rectangle(height=0.46    ,							width=0.46       ).set_stroke(width=0       )

         __lowerCamelCase            =    Rectangle(height=0.25    ,							width=0.25       )

         __lowerCamelCase            =    [mem.copy() for i in range(6       )]
         __lowerCamelCase            =    [mem.copy() for i in range(6       )]
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    VGroup(a    ,							a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    Text('''CPU'''    ,							font_size=24       )
         __lowerCamelCase            =    Group(a    ,							a       ).arrange(a    ,							buff=0.5    ,							aligned_edge=a       )
         cpu.move_to([-2.5, -0.5, 0]       )
         self.add(a       )

         __lowerCamelCase            =    [mem.copy() for i in range(4       )]
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    Text('''GPU'''    ,							font_size=24       )
         __lowerCamelCase            =    Group(a    ,							a       ).arrange(a    ,							buff=0.5    ,							aligned_edge=a       )
         gpu.move_to([-1, -1, 0]       )
         self.add(a       )

         __lowerCamelCase            =    [mem.copy() for i in range(6       )]
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )

         __lowerCamelCase            =    Text('''Model'''    ,							font_size=24       )
         __lowerCamelCase            =    Group(a    ,							a       ).arrange(a    ,							buff=0.5    ,							aligned_edge=a       )
         model.move_to([3, -1.0, 0]       )
         self.add(a       )

         __lowerCamelCase            =    []
         __lowerCamelCase            =    []

         for i, rect in enumerate(a       ):
             __lowerCamelCase            =    fill.copy().set_fill(a    ,							opacity=0.8       )
             target.move_to(a       )
             model_arr.append(a       )

             __lowerCamelCase            =    Rectangle(height=0.46    ,							width=0.46       ).set_stroke(width=0.0       ).set_fill(a    ,							opacity=0.8       )
             cpu_target.move_to(cpu_left_col_base[i]       )
             model_cpu_arr.append(a       )

         self.add(*a    ,							*a       )

         __lowerCamelCase            =    [meta_mem.copy() for i in range(6       )]
         __lowerCamelCase            =    [meta_mem.copy() for i in range(6       )]
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    VGroup(a    ,							a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    Text('''Disk'''    ,							font_size=24       )
         __lowerCamelCase            =    Group(a    ,							a       ).arrange(a    ,							buff=0.5    ,							aligned_edge=a       )
         disk.move_to([-4, -1.25, 0]       )
         self.add(a    ,							a       )

         __lowerCamelCase            =    Square(side_length=2.2       )
         key.move_to([-5, 2, 0]       )

         __lowerCamelCase            =    MarkupText(
             f"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""    ,							font_size=18    ,							)

         key_text.move_to([-5, 2.4, 0]       )

         self.add(a    ,							a       )

         __lowerCamelCase            =    MarkupText(
             f"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""    ,							font_size=18    ,							)

         blue_text.next_to(a    ,							DOWN * 2.4    ,							aligned_edge=key_text.get_left()       )
         self.add(a       )

         __lowerCamelCase            =    MarkupText(
             f"""Now watch as an input is passed through the model\nand how the memory is utilized and handled."""    ,							font_size=24    ,							)
         step_a.move_to([2, 2, 0]       )

         self.play(Write(a       )       )

         __lowerCamelCase            =    Square(0.3       )
         input.set_fill(a    ,							opacity=1.0       )
         input.set_stroke(width=0.0       )
         input.next_to(model_base[0]    ,							a    ,							buff=0.5       )

         self.play(Write(a       )       )

         input.generate_target()
         input.target.next_to(model_arr[0]    ,							direction=a    ,							buff=0.02       )
         self.play(MoveToTarget(a       )       )

         self.play(FadeOut(a       )       )

         __lowerCamelCase            =    Arrow(start=a    ,							end=a    ,							color=a    ,							buff=0.5       )
         a.next_to(model_arr[0].get_left()    ,							a    ,							buff=0.2       )

         model_cpu_arr[0].generate_target()
         model_cpu_arr[0].target.move_to(gpu_rect[0]       )

         __lowerCamelCase            =    MarkupText(
             f"""As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back."""    ,							font_size=24    ,							)
         step_a.move_to([2, 2, 0]       )

         self.play(Write(a    ,							run_time=3       )       )

         __lowerCamelCase            =    {'''run_time''': 1, '''fade_in''': True, '''fade_out''': True, '''buff''': 0.02}

         self.play(
             Write(a       )    ,							Circumscribe(model_arr[0]    ,							color=a    ,							**a       )    ,							Circumscribe(model_cpu_arr[0]    ,							color=a    ,							**a       )    ,							Circumscribe(gpu_rect[0]    ,							color=a    ,							**a       )    ,							)
         self.play(MoveToTarget(model_cpu_arr[0]       )       )

         __lowerCamelCase            =    a.copy()
         for i in range(6       ):
             a_c.next_to(model_arr[i].get_right() + 0.02    ,							a    ,							buff=0.2       )

             input.generate_target()
             input.target.move_to(model_arr[i].get_right() + 0.02       )

             __lowerCamelCase            =    AnimationGroup(
                 FadeOut(a    ,							run_time=0.5       )    ,							MoveToTarget(a    ,							run_time=0.5       )    ,							FadeIn(a    ,							run_time=0.5       )    ,							lag_ratio=0.2       )

             self.play(a       )

             model_cpu_arr[i].generate_target()
             model_cpu_arr[i].target.move_to(cpu_left_col_base[i]       )

             if i < 5:
                 model_cpu_arr[i + 1].generate_target()
                 model_cpu_arr[i + 1].target.move_to(gpu_rect[0]       )
                 if i >= 1:
                     __lowerCamelCase            =    0.7

                 self.play(
                     Circumscribe(model_arr[i]    ,							**a       )    ,							Circumscribe(cpu_left_col_base[i]    ,							**a       )    ,							Circumscribe(cpu_left_col_base[i + 1]    ,							color=a    ,							**a       )    ,							Circumscribe(gpu_rect[0]    ,							color=a    ,							**a       )    ,							Circumscribe(model_arr[i + 1]    ,							color=a    ,							**a       )    ,							)
                 if i < 1:
                     self.play(
                         MoveToTarget(model_cpu_arr[i]       )    ,							MoveToTarget(model_cpu_arr[i + 1]       )    ,							)
                 else:
                     self.play(
                         MoveToTarget(model_cpu_arr[i]    ,							run_time=0.7       )    ,							MoveToTarget(model_cpu_arr[i + 1]    ,							run_time=0.7       )    ,							)
             else:
                 model_cpu_arr[i].generate_target()
                 model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]       )
                 input.generate_target()
                 input.target.next_to(model_arr[-1].get_right()    ,							RIGHT + 0.02    ,							buff=0.2       )

                 self.play(
                     Circumscribe(model_arr[-1]    ,							color=a    ,							**a       )    ,							Circumscribe(cpu_left_col_base[-1]    ,							color=a    ,							**a       )    ,							Circumscribe(gpu_rect[0]    ,							color=a    ,							**a       )    ,							)

                 self.play(MoveToTarget(model_cpu_arr[i]       )       )

             __lowerCamelCase            =    a_c
             __lowerCamelCase            =    a_c.copy()

         input.generate_target()
         input.target.next_to(model_base[-1]    ,							RIGHT + 0.02    ,							buff=0.5       )
         self.play(
             FadeOut(a       )    ,							FadeOut(a    ,							run_time=0.5       )    ,							)

         __lowerCamelCase            =    MarkupText(f"""Inference on a model too large for GPU memory\nis successfully completed."""    ,							font_size=24       )
         step_a.move_to([2, 2, 0]       )

         self.play(Write(a    ,							run_time=3       )    ,							MoveToTarget(a       )       )

         self.wait()
